Abstract
We demonstrate the use of agent-based models to simulate the interactions of two mobile dating applications that possess divergent interaction features. We reproduce several expected outcomes when compared to extant literature. We also demonstrate the use of a standard social network analysis technique—the network regression, Multiple Regression Quadratic Assignment Procedure—in conducting a principled and interpretable comparison between the two models with strong results. This combined approach is novel and allows complex system modelers who utilize agent-based models to reduce their reliance on idealized network structures (small world, scale-free, erdos-renyi) when applying underlying network interactions to agent-based models that can often skew results and mislead from a full picture of system-level properties. This work serves as a proof-of-concept in the integration of classical social network analysis methods and contemporary agent-based modeling to compare software designs and to enhance the policy-generation process of online social networks.
Highlights
Complex systems and especially complex networks are difficult to model and even more difficult to compare in a principled fashion [1,2]
As a summary measures comparison may indicate preliminary differences between models, we consider the general shape and location of the probability distributions of likes (Figure 5A). This distribution was similar by design in both Multiplicity Level 1 (M1) and Multiplicity Level 2 (M2) with male agents generating more likes than female agents M2F = 25 < M1F = 32 < M2F = 35 < M2M = 39
There was no statistical difference in the total number of likes when comparing M1 to M2 without conditioning on gender between the models
Summary
Complex systems and especially complex networks are difficult to model and even more difficult to compare in a principled fashion [1,2]. A renewed interest in the study of complex systems and the analytical methods used to study them (e.g., agent-based modeling, social network analysis, and network science) has reinvigorated numerous technical discussions [3,4]. Our effort focuses on developing a principled comparison of network agent-based models of mobile dating applications using off-the-shelf techniques as a use-case. Recent efforts in modeling dating applications through an agent-based methodology have succeeded in further illuminating this area of inquiry [5,6,7] and serve as a rich intersection of scholarship in agent-based methodology, network science, and social network analysis
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